Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …

Current speaker verification techniques rely on a neural network to extract speaker
representations. The successful x-vector architecture is a Time Delay Neural Network …

This paper summarizes the applied deep learning practices in the field of speaker
recognition, both verification and identification. Speaker recognition has been a widely used …

In this paper, we present Multi-scale Feature Aggregation Conformer (MFA-Conformer), an
easy-to-implement, simple but effective backbone for automatic speaker verification based …

Research on speaker recognition is extending to address the vulnerability in the wild
conditions, among which genre mismatch is perhaps the most challenging, for instance …

Learning robust speaker embeddings is a crucial step in speaker diarization. Deep neural
networks can accurately capture speaker discriminative characteristics and popular deep …

This paper describes the IDLab submission for the text-independent task of the Short-
duration Speaker Verification Challenge 2021 (SdSVC-21). This speaker verification …

Time delay neural network (TDNN) has been widely used in speaker verification tasks.
Recently, two TDNN-based models, including extended TDNN (E-TDNN) and factorized …

Speaker recognition systems based on Convolutional Neural Networks (CNNs) are often
built with off-the-shelf backbones such as VGG-Net or ResNet. However, these backbones …

Despite achieving satisfactory performance in speaker verification using deep neural
networks, variable-duration utterances remain a challenge that threatens the robustness of …